In many video display systems such as TV sets, video enhancement by noise reduction is performed in order to obtain essentially noise-free video sequences for display. Various noise reduction methods have been developed, but few are used in real products because such methods introduce unwanted artifacts into video frames. Most of the conventional noise reduction methods can be classified into three categories: spatial (two dimensional (2D)) noise reduction, temporal noise reduction, and three dimensional (3D) noise reduction (i.e., combination of 2D and temporal noise reduction).
Spatial noise reduction applies a filter (with a small local window) to every pixel of the current video frame. Such a filter is usually regarded as a convolution filter based on a kernel. Examples of such a filter are the mean filter, the Gaussian filter, the median filter and the sigma filter. Mean filtering is the simplest intuitive method for smoothing images and reducing noise, wherein the mean of a small local window is computed as the filtered result. Generally, a 3×3 square kernel is used, simplifying implementation. The mean filter, however, causes severe blurring of images.
Gaussian filtering uses a “bell-shaped” kernel to remove noise. Gaussian filtering is equivalent to a weighted average operation of the pixels in a small local window. However, Gaussian filtering also introduces blurring (severeness of the blurring can be controlled by the standard deviation of the Gaussian).
Median filtering is a nonlinear method. It sorts the pixels in a small local window and takes the median as the filtered result. The median filter does not create new unrealistic pixel values and preserves sharp edges. Also, an aliasing pixel value will not affect the filtered result. However, as the number of input pixels increases, the computational cost of sorting becomes too expensive for practical implementation.
To address such problems, some edge-oriented spatial filtering algorithms have been developed. These algorithms, however, require expensive hardware and introduce artifacts when edge-detection fails, especially in noisy images. Other algorithms convert images into frequency domain and reduce the high frequency components. Since image details are also high frequency components, such methods also blur the images.
Temporal noise reduction first examines motion information among the current video frame and its neighboring frames. It classifies pixels into motion region and non-motion region. In non-motion region, a filter is applied to the pixels in the current frame and its neighboring frames along the temporal axis. In motion region, the temporal filter is switched off to avoid motion blurring. Generally, temporal noise reduction is better in keeping the details and preserving edges than spatial noise reduction. The filtering performance, however, depends on the accuracy of the motion detection. Motion blur occurs if the motion detection fails. Such disadvantages limit applicability of temporal noise reduction.
There is, therefore, a need for a noise reduction method and system that reduces the motion blur.